GAN-based unsupervised domain adaptive person re-identification - Shengsheng ZHENG; Haibing YIN; Xiaofeng HUANG; Tianjie ZHANG (Dianxin kexue)
Aiming at the problem that the inaccurate clustering in the unsupervised domain adaptive pedestrian re-recognition results in low network recognition accuracy, an unsupervised domain adaptive pedestrian re-recognition method based on generative confrontation network was proposed.Firstly, the CNN model was optimized by using the batch normalization layer after the pooling layer, deleting a fully connected layer and adopting the Adam optimizer.Secondly, the cluster error was analyzed and the important parameter in the cluster was decided based on minimum error rate Bayesian decision theory.Finally, the generative adversarial network was utilized to adjust the cluster.These steps effectively improved the recognition accuracy of unsupervised domain adaptive person re-identification.In the case of the source domain Market-1501 and the target domain DukeMTMC-reID, experimental results show that mAP and Rank-1 can reach 53.7% and 71.6%, respectively.